Urban objects classification using Mueller matrix polarimetry and machine learning

Abstract

Detecting and recognizing different kinds of urban objects is an important problem, in particular, in autonomous driving. In this context, we studied the potential of Mueller matrix polarimetry for classifying a set of relevant real-world objects: vehicles, pedestrians, traffic signs, pavements, vegetation and tree trunks. We created a database with their experimental Mueller matrices measured at 1550 nm and trained two machine learning classifiers, support vector machine and artificial neural network, to classify new samples. The overall accuracy of over 95% achieved with this approach, with either models, reveals the potential of polarimetry, specially combined with other remote sensing techniques, to enhance object recognition.